Brain Inspired

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BI 187: COSYNE 2024 Neuro-AI Panel Apr 20, 2024

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Recently I was invited to moderate a panel at the annual Computational and Systems Neuroscience, or COSYNE, conference. This year was the 20th anniversary of COSYNE, and we were in Lisbon Porturgal. The panel goal was to discuss the relationship between neuroscience and AI. The panelists were Tony Zador, Alex Pouget, Blaise Aguera y Arcas, Kim Stachenfeld, Jonathan Pillow, and Eva Dyer. And I'll let them introduce themselves soon. Two of the panelists, Tony and Alex, co-founded COSYNE those 20 years ago, and they continue to have different views about the neuro-AI relationship. Tony has been on the podcast before and will return soon, and I'll also have Kim Stachenfeld on in a couple episodes. I think this was a fun discussion, and I hope you enjoy it. There's plenty of back and forth, a wide range of opinions, and some criticism from one of the audience questioners. This is an edited audio version, to remove long dead space and such. There's about 30 minutes of just panel, then the panel starts fielding questions from the audience.

COSYNE.

BI 186 Mazviita Chirimuuta: The Brain Abstracted Mar 25, 2024

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Mazviita Chirimuuta is a philosopher at the University of Edinburgh. Today we discuss topics from her new book, The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience.

She largely argues that when we try to understand something complex, like the brain, using models, and math, and analogies, for example - we should keep in mind these are all ways of simplifying and abstracting away details to give us something we actually can understand. And, when we do science, every tool we use and perspective we bring, every way we try to attack a problem, these are all both necessary to do the science and limit the interpretation we can claim from our results. She does all this and more by exploring many topics in neuroscience and philosophy throughout the book, many of which we discuss today.

0:00 - Intro 5:28 - Neuroscience to philosophy 13:39 - Big themes of the book 27:44 - Simplifying by mathematics 32:19 - Simplifying by reduction 42:55 - Simplification by analogy 46:33 - Technology precedes science 55:04 - Theory, technology, and understanding 58:04 - Cross-disciplinary progress 58:45 - Complex vs. simple(r) systems 1:08:07 - Is science bound to study stability? 1:13:20 - 4E for philosophy but not neuroscience? 1:28:50 - ANNs as models 1:38:38 - Study of mind

BI 185 Eric Yttri: Orchestrating Behavior Mar 06, 2024

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As some of you know, I recently got back into the research world, and in particular I work in Eric Yttris' lab at Carnegie Mellon University.

Eric's lab studies the relationship between various kinds of behaviors and the neural activity in a few areas known to be involved in enacting and shaping those behaviors, namely the motor cortex and basal ganglia. And study that, he uses tools like optogentics, neuronal recordings, and stimulations, while mice perform certain tasks, or, in my case, while they freely behave wandering around an enclosed space.

We talk about how Eric got here, how and why the motor cortex and basal ganglia are still mysteries despite lots of theories and experimental work, Eric's work on trying to solve those mysteries using both trained tasks and more naturalistic behavior. We talk about the valid question, "What is a behavior?", and lots more.

Yttri Lab

0:00 - Intro 2:36 - Eric's background 14:47 - Different animal models 17:59 - ANNs as models for animal brains 24:34 - Main question 25:43 - How circuits produce appropriate behaviors 26:10 - Cerebellum 27:49 - What do motor cortex and basal ganglia do? 49:12 - Neuroethology 1:06:09 - What is a behavior? 1:11:18 - Categorize behavior (B-SOiD) 1:22:01 - Real behavior vs. ANNs 1:33:09 - Best era in neuroscience

BI 184 Peter Stratton: Synthesize Neural Principles Feb 20, 2024

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Peter Stratton is a research scientist at Queensland University of Technology.

I was pointed toward Pete by a patreon supporter, who sent me a sort of perspective piece Pete wrote that is the main focus of our conversation, although we also talk about some of his work in particular - for example, he works with spiking neural networks, like my last guest, Dan Goodman.

What Pete argues for is what he calls a sideways-in approach. So a bottom-up approach is to build things like we find them in the brain, put them together, and voila, we'll get cognition. A top-down approach, the current approach in AI, is to train a system to perform a task, give it some algorithms to run, and fiddle with the architecture and lower level details until you pass your favorite benchmark test. Pete is focused more on the principles of computation brains employ that current AI doesn't. If you're familiar with David Marr, this is akin to his so-called "algorithmic level", but it's between that and the "implementation level", I'd say. Because Pete is focused on the synthesis of different kinds of brain operations - how they intermingle to perform computations and produce emergent properties. So he thinks more like a systems neuroscientist in that respect. Figuring that out is figuring out how to make better AI, Pete says. So we discuss a handful of those principles, all through the lens of how challenging a task it is to synthesize multiple principles into a coherent functioning whole (as opposed to a collection of parts). Buy, hey, evolution did it, so I'm sure we can, too, right?

0:00 - Intro 3:50 - AI background, neuroscience principles 8:00 - Overall view of modern AI 14:14 - Moravec's paradox and robotics 20:50 -Understanding movement to understand cognition 30:01 - How close are we to understanding brains/minds? 32:17 - Pete's goal 34:43 - Principles from neuroscience to build AI 42:39 - Levels of abstraction and implementation 49:57 - Mental disorders and robustness 55:58 - Function vs. implementation 1:04:04 - Spiking networks 1:07:57 - The roadmap 1:19:10 - AGI 1:23:48 - The terms AGI and AI 1:26:12 - Consciousness

BI 183 Dan Goodman: Neural Reckoning Feb 06, 2024

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You may know my guest as the co-founder of Neuromatch, the excellent online computational neuroscience academy, or as the creator of the Brian spiking neural network simulator, which is freely available. I know him as a spiking neural network practitioner extraordinaire. Dan Goodman runs the Neural Reckoning Group at Imperial College London, where they use spiking neural networks to figure out how biological and artificial brains reckon, or compute.

All of the current AI we use to do all the impressive things we do, essentially all of it, is built on artificial neural networks. Notice the word "neural" there. That word is meant to communicate that these artificial networks do stuff the way our brains do stuff. And indeed, if you take a few steps back, spin around 10 times, take a few shots of whiskey, and squint hard enough, there is a passing resemblance. One thing you'll probably still notice, in your drunken stupor, is that, among the thousand ways ANNs differ from brains, is that they don't use action potentials, or spikes. From the perspective of neuroscience, that can seem mighty curious. Because, for decades now, neuroscience has focused on spikes as the things that make our cognition tick.

We count them and compare them in different conditions, and generally put a lot of stock in their usefulness in brains.

So what does it mean that modern neural networks disregard spiking altogether?

Maybe spiking really isn't important to process and transmit information as well as our brains do. Or maybe spiking is one among many ways for intelligent systems to function well. Dan shares some of what he's learned and how he thinks about spiking and SNNs and a host of other topics.

0:00 - Intro 3:47 - Why spiking neural networks, and a mathematical background 13:16 - Efficiency 17:36 - Machine learning for neuroscience 19:38 - Why not jump ship from SNNs? 23:35 - Hard and easy tasks 29:20 - How brains and nets learn 32:50 - Exploratory vs. theory-driven science 37:32 - Static vs. dynamic 39:06 - Heterogeneity 46:01 - Unifying principles vs. a hodgepodge 50:37 - Sparsity 58:05 - Specialization and modularity 1:00:51 - Naturalistic experiments 1:03:41 - Projects for SNN research 1:05:09 - The right level of abstraction 1:07:58 - Obstacles to progress 1:12:30 - Levels of explanation 1:14:51 - What has AI taught neuroscience? 1:22:06 - How has neuroscience helped AI?

BI 182: John Krakauer Returns… Again Jan 19, 2024

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Check out my free video series about what's missing in AI and Neuroscience

John Krakauer has been on the podcast multiple times (see links below). Today we discuss some topics framed around what he's been working on and thinking about lately. Things like

Whether brains actually reorganize after damage
The role of brain plasticity in general
The path toward and the path not toward understanding higher cognition
How to fix motor problems after strokes
AGI
Functionalism, consciousness, and much more.

Relevant links:

John's Lab.
Twitter: @blamlab
Related papers
- What are we talking about? Clarifying the fuzzy concept of representation in neuroscience and beyond.
- Against cortical reorganisation.
Other episodes with John:

Time stamps 0:00 - Intro 2:07 - It's a podcast episode! 6:47 - Stroke and Sherrington neuroscience 19:26 - Thinking vs. moving, representations 34:15 - What's special about humans? 56:35 - Does cortical reorganization happen? 1:14:08 - Current era in neuroscience

BI 181 Max Bennett: A Brief History of Intelligence Dec 25, 2023

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By day, Max Bennett is an entrepreneur. He has cofounded and CEO'd multiple AI and technology companies. By many other countless hours, he has studied brain related sciences. Those long hours of research have payed off in the form of this book, A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains.

Three lines of research formed the basis for how Max synthesized knowledge into the ideas in his current book: findings from comparative psychology (comparing brains and minds of different species), evolutionary neuroscience (how brains have evolved), and artificial intelligence, especially the algorithms developed to carry out functions. We go through I think all five of the breakthroughs in some capacity. A recurring theme is that each breakthrough may explain multiple new abilities. For example, the evolution of the neocortex may have endowed early mammals with the ability to simulate or imagine what isn't immediately present, and this ability might further explain mammals' capacity to engage in vicarious trial and error (imagining possible actions before trying them out), the capacity to engage in counterfactual learning (what would have happened if things went differently than they did), and the capacity for episodic memory and imagination.

The book is filled with unifying accounts like that, and it makes for a great read. Strap in, because Max gives a sort of masterclass about many of the ideas in his book.

Twitter:
- @maxsbennett
Book:
- A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains.

0:00 - Intro 5:26 - Why evolution is important 7:22 - Maclean's triune brain 14:59 - Breakthrough 1: Steering 29:06 - Fish intelligence 40:38 - Breakthrough 3: Mentalizing 52:44 - How could we improve the human brain? 1:00:44 - What is intelligence? 1:13:50 - Breakthrough 5: Speaking

BI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding Dec 11, 2023

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Welcome to another special panel discussion episode.

I was recently invited to moderate at discussion amongst 6 people at the annual Aspirational Neuroscience meetup. Aspirational Neuroscience is a nonprofit community run by Kenneth Hayworth. Ken has been on the podcast before on episode 103. Ken helps me introduce the meetup and panel discussion for a few minutes. The goal in general was to discuss how current and developing neuroscience technologies might be used to decode a nontrivial memory from a static connectome - what the obstacles are, how to surmount those obstacles, and so on.

There isn't video of the event, just audio, and because we were all sharing microphones and they were being passed around, you'll hear some microphone type noise along the way - but I did my best to optimize the audio quality, and it turned out mostly quite listenable I believe.

Aspirational Neuroscience
Panelists:
- Anton Arkhipov, Allen Institute for Brain Science.
  - @AntonSArkhipov
- Konrad Kording, University of Pennsylvania.
  - @KordingLab
- Tomás Ryan, Trinity College Dublin.
  - @TJRyan_77
- Srinivas Turaga, Janelia Research Campus.
- Dong Song, University of Southern California.
  - @dongsong
- Zhihao Zheng, Princeton University.
  - @zhihaozheng

0:00 - Intro 1:45 - Ken Hayworth 14:09 - Panel Discussion

BI 179 Laura Gradowski: Include the Fringe with Pluralism Nov 27, 2023

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Laura Gradowski is a philosopher of science at the University of Pittsburgh. Pluralism is roughly the idea that there is no unified account of any scientific field, that we should be tolerant of and welcome a variety of theoretical and conceptual frameworks, and methods, and goals, when doing science. Pluralism is kind of a buzz word right now in my little neuroscience world, but it's an old and well-trodden notion... many philosophers have been calling for pluralism for many years. But how pluralistic should we be in our studies and explanations in science? Laura suggests we should be very, very pluralistic, and to make her case, she cites examples in the history of science of theories and theorists that were once considered "fringe" but went on to become mainstream accepted theoretical frameworks. I thought it would be fun to have her on to share her ideas about fringe theories, mainstream theories, pluralism, etc.

We discuss a wide range of topics, but also discuss some specific to the brain and mind sciences. Laura goes through an example of something and someone going from fringe to mainstream - the Garcia effect, named after John Garcia, whose findings went agains the grain of behaviorism, the dominant dogma of the day in psychology. But this overturning only happened after Garcia had to endure a long scientific hell of his results being ignored and shunned. So, there are multiple examples like that, and we discuss a handful. This has led Laura to the conclusion we should accept almost all theoretical frameworks, We discuss her ideas about how to implement this, where to draw the line, and much more.

Laura's page at the Center for the Philosophy of Science at the University of Pittsburgh.

Facing the Fringe.

Garcia's reflections on his troubles: Tilting at the Paper Mills of Academe

0:00 - Intro 3:57 - What is fringe? 10:14 - What makes a theory fringe? 14:31 - Fringe to mainstream 17:23 - Garcia effect 28:17 - Fringe to mainstream: other examples 32:38 - Fringe and consciousness 33:19 - Words meanings change over time 40:24 - Pseudoscience 43:25 - How fringe becomes mainstream 47:19 - More fringe characteristics 50:06 - Pluralism as a solution 54:02 - Progress 1:01:39 - Encyclopedia of theories 1:09:20 - When to reject a theory 1:20:07 - How fringe becomes fringe 1:22:50 - Marginilization 1:27:53 - Recipe for fringe theorist

BI 178 Eric Shea-Brown: Neural Dynamics and Dimensions Nov 13, 2023

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Eric Shea-Brown is a theoretical neuroscientist and principle investigator of the working group on neural dynamics at the University of Washington. In this episode, we talk a lot about dynamics and dimensionality in neural networks... how to think about them, why they matter, how Eric's perspectives have changed through his career. We discuss a handful of his specific research findings about dynamics and dimensionality, like how dimensionality changes when one is performing a task versus when you're just sort of going about your day, what we can say about dynamics just by looking at different structural connection motifs, how different modes of learning can rely on different dimensionalities, and more.We also talk about how he goes about choosing what to work on and how to work on it. You'll hear in our discussion how much credit Eric gives to those surrounding him and those who came before him - he drops tons of references and names, so get ready if you want to follow up on some of the many lines of research he mentions.

0:00 - Intro 4:15 - Reflecting on the rise of dynamical systems in neuroscience 11:15 - DST view on macro scale 15:56 - Intuitions 22:07 - Eric's approach 31:13 - Are brains more or less impressive to you now? 38:45 - Why is dimensionality important? 50:03 - High-D in Low-D 54:14 - Dynamical motifs 1:14:56 - Theory for its own sake 1:18:43 - Rich vs. lazy learning 1:22:58 - Latent variables 1:26:58 - What assumptions give you most pause?

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